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Using Locations for Online-Behaviour
Prediction with Sparse Data
Nishant Oli
Location dependent User Engagement
When are you most likely to engage with your phone?
❏ At your home!
❏ At your workplace!
❏ Commuting from workplace to home!
What do we learn from our Data?
1.5x
time CTR
at Residential
Locations over
Commercial Locations
2x
time user engagement
at residential locations
over Commercial Locations
A comparison between the Residential & Commercial Locations, we
learn
How to actually incorporate location as feature to
the model?
Raw Location Data - TBs of data flowing in..
● If we consider a scenario where we get location signal per
minute, it means in a day it will get 86400 location signals per
day.
● Imagine the memory footprint of a model taking data of this
scale.
● How to make model make inferences from these raw logs? since
these are latitude & longitudes. (not defined in euclidean space)
Simple solution
● Cluster the location for a user and attribute a request to the
location.
● This is simple to implement & has decent accuracy.
● Problem is it assumes there’s dense user-level data, and is not a
scalable solution for us since we get location data sparsely.
How did we do it?
What if we have labels like these?
Yeah right that’s our Outer Ring Road in Bangalore (Results from our model.)
Commercial Locations Residential Locations
Location Classification
But isn't Classification Defined for Discrete Values!
How can we actually convert location, which is continuous in [-90,
90] & [-180,180] space, to a discrete space?
Wait! Classification means labelled data..
Well buying the labels for location is difficult to get (policies of
countries, data quality issues) & too expensive.
We can actually work it out without the labels, thanks to
unsupervised learning algorithms.
Based on the location signal from the cell(location), we classify each
location in commercial, residential & other locations using the features we
define.
Location
Data
Useri
Request
We just use the Location Data for this..
So what’s the model?
We’ll share the details about the model, in the presentation along with
● Algorithm used in detail
● How did we deal with the sparsity?
● Since we get billions of requests daily, how we optimized our model
using Spark & Scala?
With the same amount of data we classified more
than 35 Million locations..
Now, we can recall 84 out of 100 location
correctly!
How does my area look?
Live Demo!
Bangalore!
Mountain View - SF!
Fig 1 -2 showing offices of Google & Facebook (Orange color) in SF, Fig 3 Green Glen Layout (Purple dots) & Fig 4 InMobi’s
office (Blue Dots).
Wor
k
Home
Google Facebook
Some snaps of the results
Signals
1.5X CTR in Residential
Locations over Commercial
Locations Insights
2.5X User engagement at
Commercial vs Residential
Use Cases
Targeting at Scale
Target users living or working near
Walmart, Starbucks etc
What can we do better now?
Questions?
Thanks!

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Anthill 2019

  • 1. Using Locations for Online-Behaviour Prediction with Sparse Data Nishant Oli
  • 2. Location dependent User Engagement When are you most likely to engage with your phone? ❏ At your home! ❏ At your workplace! ❏ Commuting from workplace to home!
  • 3. What do we learn from our Data? 1.5x time CTR at Residential Locations over Commercial Locations 2x time user engagement at residential locations over Commercial Locations A comparison between the Residential & Commercial Locations, we learn
  • 4. How to actually incorporate location as feature to the model?
  • 5. Raw Location Data - TBs of data flowing in.. ● If we consider a scenario where we get location signal per minute, it means in a day it will get 86400 location signals per day. ● Imagine the memory footprint of a model taking data of this scale. ● How to make model make inferences from these raw logs? since these are latitude & longitudes. (not defined in euclidean space)
  • 6. Simple solution ● Cluster the location for a user and attribute a request to the location. ● This is simple to implement & has decent accuracy. ● Problem is it assumes there’s dense user-level data, and is not a scalable solution for us since we get location data sparsely.
  • 7. How did we do it?
  • 8. What if we have labels like these? Yeah right that’s our Outer Ring Road in Bangalore (Results from our model.) Commercial Locations Residential Locations
  • 9. Location Classification But isn't Classification Defined for Discrete Values! How can we actually convert location, which is continuous in [-90, 90] & [-180,180] space, to a discrete space?
  • 10. Wait! Classification means labelled data.. Well buying the labels for location is difficult to get (policies of countries, data quality issues) & too expensive. We can actually work it out without the labels, thanks to unsupervised learning algorithms.
  • 11. Based on the location signal from the cell(location), we classify each location in commercial, residential & other locations using the features we define. Location Data Useri Request We just use the Location Data for this..
  • 12. So what’s the model? We’ll share the details about the model, in the presentation along with ● Algorithm used in detail ● How did we deal with the sparsity? ● Since we get billions of requests daily, how we optimized our model using Spark & Scala?
  • 13. With the same amount of data we classified more than 35 Million locations..
  • 14. Now, we can recall 84 out of 100 location correctly!
  • 15. How does my area look? Live Demo!
  • 18. Fig 1 -2 showing offices of Google & Facebook (Orange color) in SF, Fig 3 Green Glen Layout (Purple dots) & Fig 4 InMobi’s office (Blue Dots). Wor k Home Google Facebook Some snaps of the results
  • 19. Signals 1.5X CTR in Residential Locations over Commercial Locations Insights 2.5X User engagement at Commercial vs Residential Use Cases Targeting at Scale Target users living or working near Walmart, Starbucks etc What can we do better now?